Classification of hyperspectral data from urban areas based on extended morphological profiles
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Abstract
Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral…
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Topics
Keywords
- Hyperspectral imaging
- Pattern recognition (psychology)
- Preprocessor
- Artificial intelligence
- Computer science
- Principal component analysis
- Mathematical morphology
- Feature extraction
UN Sustainable Development Goals
- Sustainable cities and communities
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